Semantic Segmentation.
We evaluate semantic segmentation performance on the ADE20K [1]} dataset. Following prior works [2]}, [3]}, we initialize the UperNet framework [4]} using our pretrained model and finetune the segmentation model end-to-end.
The model is optimized using AdamW for 80k iterations with an initial learning rate of 1e-4 and a batch size of 16.
We set the weight decay to 0.05 and layer-wise learning rate decay to 0.85.
The results are shown in Table REF . Our method is able to outperform competitive representation learning baselines such as DINO, MoCo-v3 and BEiT.
Our model is even comparable to MAE, which is trained with a much heavier schedule (300 epochs vs. 1600 epochs).
By scaling the training data to the larger ImageNet-22K dataset while keeping the total number of iterations unchanged, our model performance improves by 0.5 mIoU, surpassing all prior arts by a significant margin. This indicates that our model scales well with data.
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